When extracting three-dimensional information over an extended depth of field in imaging systems, distance to a point in a scene can be estimated from its location in two or more images captured simultaneously. The three dimensional (3D) position of the point can be computed from basic geometric relationships when the 3D relationship between the imagers is known. The challenge in computing spatial location from multiple images, often referred to as stereo correlation or stereo depth computation, is automatically and accurately associating the mapping of a point in one image with its mapping in another image. This is most often done by correlating image features from one image to one or more others. The underlying assumption in all stereo matching methods, however, is that there must be some identifiable local contrast or feature in the image in order to match that point to its location in another image. Therefore a problem arises when there is no local contrast or feature in the image because of misfocus—stereo matching does not produce accurate results in regions of an image that are out of focus.
The conventional means for extending the focal depth of an image is to reduce the diameter of the camera's lens's pupil (“stopping down”). However, two side effects restrict the usefulness of the technique. First, the sensitivity of the imaging system is reduced by a factor equal to the square of the pupil diameter ratio. Second, the maximum spatial frequency response is reduced a factor equal to the pupil diameter ratio, which limits the resolution and contrast in the image. There is thus a tradeoff between depth of field, exposure time, and overall contrast in conventional imaging systems. In the case of a multiple camera ranging system, the net effect will be a compromise between stereoscopic depth accuracy and working range.